iCreatAI News
Back to main site
Skip to content
Only Models#Open source
  1. The Decoder85

    Meituan releases a 1.6-trillion-parameter agent model that never touched an Nvidia GPU

    Meituan has released the weights for LongCat-2.5, a 1.6-trillion-parameter mixture-of-experts model for autonomous agent and coding tasks that activates roughly 48 billion parameters per token and was trained entirely on Chinese hardware. It handles up to one million tokens of text, image, and video input via API, and Meituan reports 88.4 on Terminal Bench v2.1 and 64.6 on DeepSWE, close to but below Claude Opus 5 and GPT-5.6 Sol on several benchmarks. The weights are MIT-licensed, and the self-hosting guide describes a 194-chip Huawei Ascend Atlas A2 cluster with no Nvidia GPU guidance.

    Why it matters: It documents a frontier-scale open-weight agent model trained and served entirely on Chinese accelerators, with benchmark comparisons against leading Western models.

  1. Hacker News AI88

    Edge0-145B: Frontier AI Capabilities on Your Own Computer

    The company open-sourced Edge0-145B and Edge0-120B, describing Edge0-145B as the first open-source 145-billion-parameter on-device model, alongside a unified inference framework and deployment tools. It reports 29GB peak memory, 80+ tokens/s decoding, 1,100+ tokens/s prefill, roughly 120W typical power and 128K–256K context on a compatible laptop, with benchmark scores compared against named cloud models. The release also describes shared-matrix sparse experts, tiered GPU/system-memory/SSD loading and CPU-GPU co-scheduling, plus local demonstrations in video, Blender 3D scenes, games, drone control and PCB design.

    Why it matters: It offers a detailed, benchmark-backed look at how a 100B+ model is engineered to run locally, useful for readers weighing on-device versus cloud AI.

  2. Hugging Face Blog66

    LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B

    IBM researchers introduce LLMON (LLM Object Notation), an XML-like markup language that uses reserved special-token delimiters to explicitly mark instructions, data, requirements and execution bindings in an LLM's context, addressing the brittleness of JSON-style formats for causal models. They release two experimental preview models derived from Granite 4.1 base checkpoints (granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview) plus a GitHub toolkit, reporting that on the OPI benchmark LLMON models avoid 47% more indirect prompt injection attacks than the best frontier model tested while maintaining general capability on par with Granite 4.1 Instruct. The models ship with their own chat template and lossless JSON-to-LLMON converters, and the post notes the previews are for exploration, not production.

    Why it matters: Useful for teams weighing structured context formats against JSON and for anyone evaluating prompt-injection defenses in small open models.

  1. Amazon Science62

    Mitra-v2: Scaling synthetic pretraining for tabular foundation models

    Mitra-v2 is a second-generation tabular foundation model for classification and regression, pretrained entirely on synthetic tables with 10x as many rows and roughly 3x as many columns as Mitra-v1, plus a new hybrid SCM prior and hybrid optimizer. On TabArena it leads the single-model field, beating Mitra-v1 on 32 of 33 datasets where both run natively and completing all 51 datasets within the one-hour budget, and fine-tuned Mitra-v2 ranks first on ScoringBench. The fine-tuned models and inference/fine-tuning code are released under Apache-2.0 on Hugging Face.

    Why it matters: Useful for readers tracking tabular foundation models, as it reports concrete benchmark rankings and an open Apache-2.0 release.

  1. Hugging Face Blog62

    New NVIDIA Cosmos 3 Open Models for Interactive World Simulation

    NVIDIA's Cosmos world model platform adds four new open-weight checkpoints to its Cosmos 3 family: Cosmos3-Nano-Transfer-Auto for multi-view driving data with varied weather, lighting and time of day; Cosmos3-Nano-Sim-Auto for streaming synchronized camera and LiDAR driving simulation; Cosmos3-Nano-Sim-Transfer for turning simulator controls and text prompts into photorealistic video; and Cosmos3-Nano-Sim-Bimanual for predicting video from robot or camera actions. All four are post-trained examples of Cosmos 3 Nano with 16B trainable parameters and use OpenMDW1.1, permitting commercial and non-commercial use under its terms. The post also details a four-stage training recipe (bidirectional adaptation, teacher forcing, consistency distillation, self-forcing) and post-training guidance for adapting the models to new sensor rigs and embodiments.

    Why it matters: It gives a concrete inventory of newly released open-weight world-model checkpoints plus the training recipe, useful for researchers weighing them for robotics or driving simulation.

  2. Artificial Analysis Articles62

    Nanbeige has released Nanbeige4.3-33B scoring 20 on the Intelligence Index

    Nanbeige, the foundation model research team at BOSS Zhipin (operated by Kanzhun Limited), has released Nanbeige4.3-33B, an Apache 2.0 open-weights dense reasoning model with text-only input and output and a 524K-token context window. It scores 20 on the Artificial Analysis Intelligence Index v5, matching Qwen3.8 27B (xhigh) at 20, with relative strengths in terminal use (30% on Terminal-Bench 4.0 vs. 6%) and physics reasoning (16% on CritPt v2's Hard Subset vs. 4%), but trails on AA-Briefcase v1.1 (1162 vs. 1391 Elo), FrontierCode 2.0 (11% vs. 15%) and HLE-Diamond (7% vs. 15%). It shows high abstention and low hallucination on Omniscience (18% vs. 29% hallucination rate, declining 71% of questions) and used 158K output tokens per Intelligence Index task vs. 110K for Qwen3.8 27B.

    Why it matters: Useful for readers tracking small open-weights models, since it sets a new Pareto point for Intelligence Index versus total parameters and details trade-offs against Qwen3.8 27B.

  1. Hugging Face Blog71

    A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

    Gradium details Phonon, a small on-device text-to-speech model released two weeks ago that needs no server or network, with Phonon 31m (English) under Apache 2.0 and Phonon 40m covering English, French, German, Spanish and Portuguese under a non-commercial license. The models, about 65 MB and 75 MB, offer over 100 built-in voices plus voice cloning and voice design, and on Hugging Face's Open TTS Leaderboard Phonon ranks first in German and Spanish on intelligibility and tops the CPU streaming benchmark on time-to-first audio. The post also covers the ptts inference framework, Transformers integration from v5.21, launch demos, and more than 200,000 downloads in the first two weeks.

    Why it matters: It offers a detailed look at how a small on-device TTS model was built, benchmarked and integrated into developer tooling.

  2. MarkTechPost76

    Ricursive AI Releases Ripple-65B Preview: A 65B Dense Open-Weight Model Scoring 80.5 on Terminal-Bench 2.1

    Ricursive Intelligence released Ripple-65B Preview, a 65B dense open-weight model pretrained from scratch on 12T tokens with up to 1M context, shipping three Apache 2.0 checkpoints (instruction-tuned BF16, NVFP4, and BF16 base). Ricursive reports 80.5 on Terminal-Bench 2.1, the top self-reported score among US open-weight models, while trailing larger MoE rivals on SciCode (39.4) and DeepSWE 1.1 (44.3), with full R&D cost stated at $32M. Weights are on Hugging Face, with Together AI, GMI Cloud and OpenRouter listed at launch and an experimental NVFP4 build targeting NVIDIA DGX Spark.

    Why it matters: Useful for readers tracking open-weight agentic coding models, since it pairs self-reported benchmark claims with a detailed architecture, cost and deployment breakdown.

  3. Hugging Face Blog60

    Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

    Dharma-AI introduces the Dharma Field Extractor, a 2-billion-parameter model built on DharmaOCR-LITE v1.1 with a LoRA adapter trained on 43,384 examples, which takes a document image plus either an open request or an exact schema and returns typed JSON, including null for fields not present. On a 546-document, 1,638-case benchmark it scored 0.834 field-level F1, above every frontier configuration tested (Claude Opus 5 at 0.816 to GPT-5.6 Luna at 0.741), while running self-hosted on a single L40S at a cost the company reports as 6 to 147 times lower than the APIs compared. The company frames the result as evidence that a small model trained close to a bounded task can beat a general frontier model on that task.

    Why it matters: Useful for teams weighing self-hosted specialized extraction against frontier APIs, though the benchmark and cost figures come from the vendor itself.

  1. Mistral AI82

    Le Chonk: Built for Coders, Trusted by Defenders.

    Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model trained from scratch in Europe, reporting leading open-weight results on coding benchmarks such as FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), plus 51% on the Artificial Analysis Cyber Index. It offers three reasoning modes (Low, Medium, High) and is priced at $0.70 input / $0.07 cached input / $2.10 output per million tokens under a modified MIT license that requires a separate commercial license for Model-as-a-Service offerings.

    Why it matters: The post details benchmark scores, pricing, licensing terms and deployment options for a flagship open-weight model, useful for developers weighing coding performance against cost and control.

  1. Hugging Face Blog78

    NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

    NVIDIA released Nemotron 3.5 Transcribe, an open-weight, promptable model that handles transcription in 40 languages, translation of 31 to or from English, speaker attribution in 13 languages, and audio question answering in one checkpoint, running on a single NVIDIA GPU. It ranks first among open-weight models on the Hugging Face Open ASR Leaderboard for English at 5.33% average WER and posts the lowest diarization error rate on David AI's 21-language leaderboard. The release also includes Granary v2, an open data curation pipeline covering 463,062 hours across 48 languages, and NeMo Speech fine-tuning recipes that cut held-out WER by 45.8% relative to the best prompt in a medical case study.

    Why it matters: It documents concrete benchmark standings, an open data pipeline, and a reproducible fine-tuning recipe for teams evaluating or adapting open speech models.

  2. Hugging Face Blog78

    Build specialized multimodal models with LFM3

    Liquid AI introduces LFM3, a generation of Liquid Foundation Models in the 2B parameter range that places text, vision, audio, omni, and decision models on one shared backbone built from LFM3-1.7B-Base, which was pre-trained on 22T tokens and uses a tri-hybrid layer mix with a 256K context window. The release includes six 2B-class models, new in-house vision and audio encoders, and LiquidCodec, with every model fine-tunable on a single GPU and the text model fitting in about 1 GB at 4-bit precision. All LFM3 models are open-weight and available on Hugging Face, with fine-tuning notebooks and agent harness guides provided.

    Why it matters: The post is a detailed technical release covering architecture changes, benchmark comparisons, and runnable code for six models, useful for developers evaluating small multimodal models for on-device deployment.

  1. NVIDIA Blog73

    NVIDIA Open Models Advance Europe’s Sovereign Intelligence Across Industries

    NVIDIA is expanding its family of open models across language, robotics, biology and quantum computing, including the new Nemotron 3.5 Super VL and Nemotron 3.5 Bolt models, Nemotron Retriever models, the Kumo family for structured business data, Cosmos 3.5 world models, Alpamayo 2 Edge for autonomous vehicles, BioNeMo for healthcare and science, and Ising for quantum computing. The company says the open models give organizations a foundation they can customize with local data and deploy on infrastructure they control, with Nemotron built using data and expertise from the NVIDIA Nemotron Coalition including European partners Black Forest Labs, H-Company and Mistral. Availability details include Nemotron 3.5 Super VL on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, with Nemotron 3.5 Bolt E4B 4GB expected Friday, Oct. 23.

    Why it matters: The piece is a broad product roundup that shows how NVIDIA is positioning open models as a sovereignty-friendly foundation for European enterprises and researchers.

  1. Mistral AI67

    Introducing Timestral

    Mistral releases Timestral, its first family of time-series foundation models in four sizes from 5M to 209M parameters, with the largest ranking first on GIFT-Eval, TIME and fev-bench among pre-trained zero-shot models. Timestral produces probabilistic multivariate forecasts, supports past-only and known-future covariates, and runs on CPU without a GPU. It combines synthetic-first pre-training, covariate-aware attention and decoupled horizon forecasting, and can be fine-tuned on proprietary data through Mistral Forge.

    Why it matters: Useful for teams weighing time-series foundation models, since it reports benchmark standings, a size-versus-accuracy trade-off and CPU latency figures alongside the adaptation options.

You have reached the end